There is also another line of research that offers methods to approximate the filtering densities via samples. Among these methods is particle filtering which is also known as the sequential Monte Carlo [1]}. Although these methods can approximate the optimal filters for almost all types of filtering problems, to avoid particle weight collapse, the number of particles in these methods often scales exponentially with the dimension of the problem [2]}, [3]}, [4]}. Another class of filters are so-called assumed density filters [5]} which include sigma-point filters such as unscented Kalman filter [6]} and Gaussian approximation of nonlinear filter [7]}, [8]}. These filters can be seen as special cases of projection filters [9]}.
